code: def load_weights(model, weights):
      """Load weights to Conv2D, BatchNorm, Dense layers of model sequentially"""
      layer_index = 0
      groupped_weights = group_weights(weights)
      for layer in model.layers:
          if isinstance(layer, (Conv2D, BatchNormalization, Dense)):
              print(layer)
              layer.set_weights(groupped_weights[layer_index])
              layer_index += 1
  
file_path: efficientnet/scripts/load_efficientnet.py
imports: import argparse
  import sys
  import numpy
  import tensorflow
  import efficientnet.keras
  from keras.layers import BatchNormalization, Conv2D, Dense
label_desc: Use image node_id 3 for calling a global function with example usage: load_weights(model, weights) without return types